2.1. Semantic networks in the mental lexicon

The aim of studying the mental lexicon is to understand the knowledge we gain on words throughout our lives. The model of lexical networks has been widely developed according to the precepts of network theory and it represents the relations established between words that make up the lexicon within the semantic memory (De Deyne et al., Reference De Deyne, Kenett, Anaki, Faust, Navarro and Jones2017). The purpose of far-reaching networks based on data of continuous free association (De Deyne & Storms, Reference De Deyne and Storms2008) or computer simulations (Borge-Holthoefer & Arenas, Reference Borge-Holthoefer and Arenas2010) is to create simulations that are as similar as possible to the overall lexicon. This approach is informed by the seminal study by Collins and Loftus (Reference Collins and Loftus1975), who contend that a network is a valid model of psychological representation for describing the mental lexicon; that is, a metaphorical representation that reflects the structure of the latent lexicon, given that it explains its properties, such as the spread of activation, the extraction of meaning through the network and the growth of the network through learning. The words in this large-scale overall network establish relations based on different kinds of properties: semantic (dog-cat); syntactical or combinatory (eat-cake); phonological and orthographic (trick-truck), pragmatic or of usage (dear-sir) (Aitchison, Reference Aitchison2012), which validate a representation of experiential semantic knowledge, a sensory-motor interaction-based one and a language-based one (Vigliocco et al., Reference Vigliocco, Meteyard, Andrews and Kousta2009).

The types of relations between network nodes have prompted some scholars to differentiate between lexical networks and semantic (or thematic) ones. The former incorporate all nature of relations between nodes and tend to be created with continuous free association data, with contextually guided relations prevailing, whereas the latter are based on hierarchical taxonomic relations. Nevertheless, the boundaries between these two types of networks are blurred, as the responses the speakers provide in the fluency tests often interfere with all the processes involved in the meaning, understood in a broad sense, which incorporate the denotative meaning (semantic memory) and combinatory or experiential processes (associations) (Sánchez-Saus Laserna, Reference Sánchez-Saus Laserna2019).

Some of the more significant processes in the organization of knowledge and the systemization of experience involve categorization, being reflected in the semantic memory’s arrangement of the lexicon in the form of categories or word groups with a similar semantic or existential content. This categorization of the lexicon permits accessing the subnetworks or partial networks whose main reason for association involves relations of meaning. This level of analysis has been referred to as a mesoscopic description of the networks (Borge-Holthoefer & Arenas, Reference Borge-Holthoefer and Arenas2010). This approach analyses partial sections of a larger structure, which in network theory are called modules or communities, and provide information on the content, qualitative properties and relations of similarity within the lexicon (De Deyne et al., Reference De Deyne, Kenett, Anaki, Faust, Navarro and Jones2017). Linguistic categories could be included within this level of analysis by identifying regions within the overall network with a certain degree of meaning-based clustering. These regions tend to be hierarchical and allow identifying well-defined subcategories or communities within them.

The paradigm of category fluency serves as a robust empirical method for investigating semantic representations and the organization of conceptual storage (Goñi et al., Reference Goñi, Arrondo, Sepulcre, Martincorena, de Mendizábal, Corominas-Murtra, Bejarano, Ardanza-Trevijano, Peraita, Wall and Villoslada2011). Recent research has emphasized the development of standardized practices for constructing networks derived from category fluency data (Christensen & Kenett, Reference Christensen and Kenett2023; Zemla & Austerweil, Reference Zemla and Austerweil2018), driven by the growing application of this methodology in diverse linguistic inquiries. Empirical findings obtained through the analysis of network structures have contributed to understanding the relation between lexical-semantic knowledge and biological variables, such as aging (Wulff et al., Reference Wulff, Hills and Mata2022) and psychological features, as creativity (Kenett & Faust, Reference Kenett and Faust2019). Additionally, this paradigm has been instrumental in examining the lexicon structure of second-language speakers, facilitating comparisons with native speakers (Feng & Liu, Reference Feng and Liu2024; Hernández Muñoz et al., Reference Hernández Muñoz, Tomé, López García, Díaz and Muñoz2025; Li et al., Reference Li, Jiang, Shang and Chen2021). For example, research indicates that native speaker networks exhibit lower modularity than those of non-native speakers, thereby enhancing connectivity within the global network (Borodkin et al., Reference Borodkin, Kenett, Faust and Mashal2016). Moreover, it has been utilized to evaluate the impact of various factors on language acquisition, such as immersion experiences (Chaouch-Orozco & Martín-Villena, Reference Chaouch-Orozco and Martín-Villena2024), or increased educational exposure and enhanced proficiency levels (Agustín-Llach, Reference Agustín-Llach2022; Quintanilla Espinoza & Kloss Medina, Reference Quintanilla Espinoza and Kloss Medina2024). Category fluency not only provides access to network structures at macro and mesoscopic levels but also allows for microscopic analyses. So, this enables the identification of specific nodes or clusters that establish highly cohesive substructures in learning new-words procedures, which in turn contribute to the network’s overall integrity (Agustín-Llach & Rubio, Reference Agustín-Llach and Rubio2024). Finally, another field of research, where the analysis of semantic network structures is contributing noteworthy findings is the assessment of the linguistic performance associated with pathological conditions, including Alzheimer’s disease (Bertola et al., Reference Bertola, Mota, Copelli, Rivero, Diniz, Romano-Silva, Ribeiro and Malloy-Diniz2014; Lerner et al., Reference Lerner, Ogrocki and Thomas2009).

The large-scale lexical networks that represent the mental lexicon have two constant structural features: they are small-world networks and have a certain degree of modularity (Borge-Holthoefer & Arenas, Reference Borge-Holthoefer and Arenas2010; Steyvers & Tenenbaum, Reference Steyvers and Tenenbaum2005). Networks with a small-world structure have a high degree of clustering and a short overall distance, which means passing from one node to another in only a few steps (as opposed to random networks in which there is little local clustering and the overall distance is short), whereby information flows rapidly and efficiently without the drawbacks of hyperconnectivity The usual way of deciding whether a network is a small-world one involves considering two structural properties, namely, the clustering coefficient (CC) and the average shortest path length (ASPL). The distance between one node and all the others tends to be short in small-world networks. Furthermore, the degree of small-worldness is established through the S metric (Humphries & Gurney, Reference Humphries and Gurney2008), which requires an equilibrium between CC and ASPL (S values higher than 1 indicate a small-world network). A network’s degree of modularity refers to its division into communities or subcategories. A network is modular when it contains a dense cluster of communities (Newman, Reference Newman2010)). While a modular network tends to be a small-world one, not all small-work networks are necessarily modular (Borodkin et al., Reference Borodkin, Kenett, Faust and Mashal2016). These basic features for structuring the network generated by the categories constitute our starting point for confirming that our data replicate prior lexical-semantic models, although differentiating conceptual categories requires a more sophisticated analysis model.

In addition to the network’s topology, it is essential to address the dynamic processes that can be observed within it. Dynamic systems within network theory refer to networks that evolve over time according to specific rules or equations (Borge-Holthoefer & Arenas, Reference Borge-Holthoefer and Arenas2010; Newman, Reference Newman2010). Consequently, explaining the dynamics of a network involves understanding the processes driving structural changes, how a particular configuration emerges and how it evolves across time. As highlighted by Siew et al. (Reference Siew, Wulff, Beckage and Kenett2019), semantic and lexical networks exhibit inherent dynamism in various ways. On one hand, linguistic input and experiences enhance lexical knowledge throughout an individual’s lifespan as part of language development. Therefore, examining changes in networks during native and non-native language acquisition and learning processes becomes essential (Agustín-Llach, Reference Agustín-Llach2022; Chaouch-Orozco & Martín-Villena, Reference Chaouch-Orozco and Martín-Villena2024). On the other hand, lexical networks also exhibit dynamic phenomena, such as lexical access processes, the spread of activation between words, memory retrieval or the identification of central nodes that individuals apparently use to navigate the network (Baronchelli et al., Reference Baronchelli, Ferrer-i-Cancho, Pastor-Satorras, Chater and Christiansen2013; Siew et al., Reference Siew, Wulff, Beckage and Kenett2019).

Our study investigates the dynamics of categoril networks in the latter sense, focusing specifically on how speakers navigate these networks – namely, the processes of selecting and retrieving lexical items during semantic fluency tasks. In this context, the network is constructed by the speaker through the retrieval of semantic memory. Thus, this research adopts a more speaker-centred perspective on network dynamics, emphasizing not only the propagation of lexical information between nodes in an established network but also the preceding processes by which the speaker generates the network’s structure. This perspective aligns with a linguistics approach grounded in speaker-based analysis.

2.2. Categorial structures

In the framework of cognitive sciences, categorization is conceived as a mental process of organizing the information obtained through the apprehension of reality, which allows us to make sense of experience. ‘Categories are kinds, and categorization occurs when the same output occurs with the same kind of input, rather than the same input’ (Harnad, Reference Harnad, Cohen and Lefebvre2005, p. 22). This process, ‘by which distinct entities are treated as equivalent, is one of the most fundamental and pervasive cognitive activities’ (Wilson & Keil, Reference Wilson and Keil2001, p. 104), hence the great interest it has generated across fields like psychology, anthropology, philosophy, AI or neuroscience, in addition to linguistics (see Aarts, Reference Aarts2006 for a review in the history of linguistics). Even very young children, who are just beginning to produce their first words, are able to use language to categorise the elements around them. The earliest words designate members of well-organized categories (Koenig & Woodward, Reference Koenig, Woodward and Gaskell2007, p. 621) and countable nouns, in particular, seem to encourage category formation and recognition of patterns (Balaban & Waxman, Reference Balaban and Waxman1997; Graham et al., Reference Graham, Kilbreath and Welder2004; Pomiechowska & Gliga, Reference Pomiechowska and Gliga2019). Neuroscience further reveals that this phenomenon has a physical correlate. Different categories have distinct neuroanatomical bases (Damasio et al., Reference Damasio, Grabowski, Tranel, Hichwa and Damasio1996; Ullman, Reference Ullman and Gaskell2007): ‘the same regions are active, at least in part, when objects from a category are recognised, named, imagined, and when reading and answering questions about them’ (Martin & Chao, Reference Martin and Chao2001, p. 199).

Categorization is such a complex phenomenon that no model has been able to provide a satisfactory answer that encompasses all categories of human knowledge (Wilson & Keil, Reference Wilson and Keil2001, p. 104). For this reason, our verbal fluency task employs four categories that fit the different structural descriptions postulated by some of the main theories of categorization, namely, the prototype theory, the exemplar view and the knowledge approach (see Taylor, Reference Taylor2005). Firstly, Animals is considered a natural category, containing exemplars that reveal a relation of existential belonging to the group (a dog is an animal) and is organized around a central prototype, understood to be a combination of typical properties according to the standard version of prototype theory (Kleiber, Reference Kleiber1990; Rosch et al., Reference Rosch, Mervis, Gray, Johnson and Boyes-Braem1976; Rosch & Mervis, Reference Rosch and Mervis1975). As opposed to the classic Aristotelian concept, these properties are not the category’s defining features (in the sense of the necessary and sufficient conditions for its definition), and not all the members have to share them. By contrast, the relation that links them is family similarity, a structure for linking the exemplars in a category without having to share a common attribute that defines it (Lewandowska-Tomaszczyk, Reference Lewandowska-Tomaszczyk, Geeraerts and Cuyckens2007, p. 146).

In turn, the knowledge approach assumes that concepts are represented and organized according to people’s world views (Berko & Bernstein, Reference Berko and Bernstein2001). As opposed to the classic approach and the prototype model, features play a subordinate role because they do not define the concept, but instead constitute the product of our underlying knowledge. This hypothesis informs the existence of a non-conventional type of categories that are referred to as ad hoc: ‘an ad hoc category is a novel category constructed spontaneously to achieve a goal relevant in the current situation (e.g., constructing tourist activities to perform in Beijing while planning a vacation)’ (Barsalou, Reference Barsalou and Hogan2010, p. 86).

As these categories are created specifically for a given and particular purpose at the time of use, exemplars of an ad hoc category do not even have to share common features, although they have a hierarchical structure, with more or less representative exemplars, the same as natural categories (Croft & Cruse, Reference Croft and Cruse2008, p. 129). The consideration of belonging stems from the fact the member fulfils an objective or ideal (Best, Reference Best2002, p. 407; Kövecses, Reference Kövecses2006, p. 27; Sawyer, Reference Sawyer2007, p. 120): for example, the members of the category foods eaten when on a diet will be judged according to the objective of being low in calories – the ideal is to have none at all – and this will determine their belonging to the category.

This internal structure is generally as stable and robust as that of familiar taxonomic categories (Cassol Soro & Boto Ferreira, Reference Cassol Soro and Boto Ferreira2017; Mauri, Reference Mauri, Blochowiak, Grisot, Durrleman and Laenzlinger2017). Nevertheless, in contrast to these, ad hoc categories are not embedded in the memory and not possible without a context (Barsalou, Reference Barsalou and Hogan2010, p. 86). Nonetheless, the repeated use of an ad hoc category renders it highly familiar and consolidates it in the memory, constituting what Barsalou (Reference Barsalou and Hogan2010, Reference Barsalou, Fiorentini, Mauri and Goria2021) calls a goal derived category. They both have a bottom-up structure, with the category being generated by the exemplar that meets the expectations held towards the category, and not vice versa (Barsalou, Reference Barsalou, Fiorentini, Mauri and Goria2021; Mauri, Reference Mauri, Blochowiak, Grisot, Durrleman and Laenzlinger2017).

This study proposes Objects laid on the table for a meal (hereinafter Objects laid on the table or simply Table) as an example of this type of category. The aim of facilitating a meal allows relating such disparate items as bread, fork, tablecloth and vase, and the most typical object will be considered the one that best suits that purpose. We therefore refer to this as an ad hoc category, although strictly speaking it is a goal derived category, understanding it to be embedded in the long-term memory because of the daily chore of laying the table.

Like Barsalou, Lakoff shows the ways in which contextual knowledge wields its influence on the formation of categories, assuming that the lists of features are insufficient for fully representing a concept: concepts are constructed on the basis of cultural criteria that may or may not be immediately obvious (Berko & Bernstein, Reference Berko and Bernstein2001). Accordingly, the radial categories proposed by Lakoff (Reference Lakoff1987) – or complex categories according to Langacker (Reference Langacker and Rudzka-Ostyn1988, pp. 134–135) – are also framed within the knowledge approach and are based on the aforementioned notion of family similarity. A word activates multiple cognitive domains, which in turn activate other conceptual networks, which need not be directly related to the former. These categories have a prototypical nature with a central member and a network of links to other members (Brugman & Lakoff, Reference Brugman, Lakoff and Geeraerts2006, p. 109). Nevertheless, ‘both central and noncentral subcategories have their own representations, and no properties of subcategories can be predicated from the central subcategory’ (Lewandowska-Tomaszczyk, Reference Lewandowska-Tomaszczyk, Geeraerts and Cuyckens2007, p. 148).

As an example of a radial category, we have chosen Games and pastimes, consisting of sundry subcategories (e.g., card games, board games, children’s games and sports), some more central than others, which do not have to be directly related to each other, as noted by Wittgenstein (Reference Wittgenstein, Laurence and Margolis1999, p. 171): ‘Consider for example the proceedings that we call “games". I mean board-games, card-games, ball-games, Olympic-games, and so on. What is common to them all? […] For if you look at them you will not see something that is common to all, but similarities, relationships, and a whole series of them at that’.

Finally, the category Countryside has a schema organization, specifically, corresponding to what De Vega (Reference De Vega1995, p. 393) calls visual schema, or to the scenes according to Mandler (Reference Mandler1984, pp. 77–78): ‘scene knowledge consists of inventory and spatial relation information that allows us to recognize a place as a living room or a playground’. Schemas are knowledge structures that represent stereotypical situations that are frequently encountered. There are acquired based on prior experience and serve as interpretative models of the new situations we face (Cifuentes Honrubia, Reference Cifuentes Honrubia1994).

Indeed, our memory stores incomplete schema consisting of fixed and variable parts (Minsky, Reference Minsky and Winston1975; Rumelhart & Ortony, Reference Rumelhart, Ortony, Anderson, Spiro and Montague1977; Rumelhart, Reference Rumelhart, Spiro, Bruce and Brewer1980 apud Téllez, Reference Téllez2005, pp. 95–97). Once the schema has been activated, the slots in it can be filled with specific information on the real or specific event described, or they can be completed with the default value typically associated with the framework in question. This default value is used when the outside world does not provide any other value and may generate false memories (Wilson & Keil, Reference Wilson and Keil2001, p. 729).

Schemas also represent knowledge at all levels of abstraction; they incorporate both episodic and semantic declarative knowledge, they are governed by an affective attitude that is reflected in memory processes and are arranged into hierarchies: schemas include others of a more elementary nature and, in turn, constitute the sub-schema of others (Crespo León, Reference Crespo León1997, p. 277; Téllez, Reference Téllez2005, p. 97).

Countryside therefore corresponds to a very general schema, which includes other more specific scenes according to their hierarchical arrangement. The examples reviewed for this category are numerous and diverse, depending on the sub-schema chosen (village, square, farm) and may refer to agents (old people, shepherd, farmer, labourers), objects (animals, plants and geographical features), activities (rest, harvest) or feelings and sensations (tranquillity, happiness), among others directly related to the proposed schema or to one of its sub-schemas.

4.1. Semantic fluency task and network construction

Participants were 680 preuniversity students (17 to 18 years old), 290 males and 390 females, all of whom are native speakers of the Spanish spoken in central Spain. They completed a semantic fluency task during a class period in their scholar institutions. The task adopts the methodological parameters from lexical availability studies in linguistics, as part of the Pan-Hispanic Lexical Availability Project (López Morales, Reference López Morales and Catalán2014), with written tests conducted for 16 categories and two minutes for each. Four prompts were chosen from the entire set.: Animals, Objects laid on the table for a meal, Games and pastimes and Countryside. During the processing of transcribing, the responses were corrected and edited according to the most recent normative orthography of Spanish (ASALE & RAE, 2010). Morphological variables were standardized under a single lemma-type (gender and number, e.g., maestro, maestra, maestros and maestras into maestro ‘teacher’) and duplicate words were removed (see Hernández Muñoz (Reference Hernández Muñoz2006) for a more comprehensive description of the data editing processes). The project encompassing this research received approval from the Ethics Committee for Research at the University of Salamanca.

Data processing and network construction have been performed with the tool LexPro (Hernández Muñoz et al., Reference Hernández Muñoz, Tomé, López García, Díaz and Muñoz2025), which directly provides the calculations specified in 5.1 for the general characterization of the categories according to their results in the semantic fluency task: total number of words (tokens), the number of different words or terms (types), and the average per informant (see Table 5). LexPro also caters for the generation of different kinds of graphs and provides traditional mathematical indices for the description of nodes and networks.

The created graphs consist of undirected bigrams, representing all pairs of words produced consecutively by the informants without considering the direction in the production chain, following traditional models of network creation based on experimental data from semantic fluency tasks (Feng & Liu, Reference Feng and Liu2023; Goñi et al., Reference Goñi, Arrondo, Sepulcre, Martincorena, de Mendizábal, Corominas-Murtra, Bejarano, Ardanza-Trevijano, Peraita, Wall and Villoslada2011; Zemla & Austerweil, Reference Zemla and Austerweil2018). To generate these graphs, the words provided by a single informant were excluded, and edges with a weight of one were removed (Borodkin et al., Reference Borodkin, Kenett, Faust and Mashal2016). The mathematical measures considered for the analysis are described in the next section. The SPSS 26 package has been used for the complementary statistical analysis.

4.2. Measures used for the analysis

The verification of each hypothesis formulated in Section 3 has involved the use of different mathematical indices. They are all described in Tables 3 and 4.

Table 3. Measures considered for each hypothesis in the mesoscopic analysis

Table 4. Measures considered for each hypothesis in the microscopic analysis

5.1. General data on categories

Table 5 presents the general results of the semantic fluency test. Animals is the most productive category, with 14721 tokens and an average of 21.65 words per informant, compared to Objects laid on the table, which records a lower average. The analysis of variance confirms that the differences observed in the number of words per informant in each category are statistically significant (F[3.2716] = 583.003, p = 0.000). Post-hoc comparisons used the Scheffé test indicate that the differences between all possible category pairs are significant (p = 0.000).

Table 5. Quantitative indices of the semantic fluency task

5.2. Mesoscopic level

5.2.1. Network size

‘The traditional measures of Average Path Length and Diameter are closely similar in Animals and Objects laid on the table, with a slightly broader network for Games and Countryside’. Considered in isolation, there is no difference between our lexical sets. With a view to increasing the explanatory power of our analysis, we have chosen to consider the number of communities they include (Table 6) and the number of nodes in each community (see Supplementary Materials). This property is assessed as a continuum: in itself, the number of nodes is not so relevant as the degree of homogeneity or heterogeneity of the community’s architecture.

Table 6. Communities in each category’s networks

Furthermore, as general properties of the four networks, we can confirm that they all have a small-world architecture (sigma > 1) and a certain degree of modularity (see Section 5.2.2.), whereby their structure responds to the required models of a mental lexicon network. As we shall see in due course, the greatest differences among the four categories are to be found in the topology of their modularity.

Animals has nine communities, six of which (66.7%) exceed the average of nodes per community, with over 30 members (between 51 and 32). Objects laid on the table has 15 communities, with 40% exceeding the average (10.87 nodes), and only the first one has more than 30 members. Games has 19 communities, with seven of them exceeding the average (36.8%), with between 50 and 22 members. Finally, out of the 31 communities in Countryside, only eight exceed the average (25.8%). This category has three very large communities (87, 77 and 58 members), seven with between 35 and 12 members and 22 with few members (15 of which have only two members).

These results indicate that, in agreement with H1, the network corresponding to the natural category is denser than all the others. Animals has fewer communities and they are more uniform in terms of the number of their members. At the other end of the scale, Countryside, has numerous communities and is highly diverse in terms of the number of nodes it contains. Like Countryside, Games creates a large network, although its communities are less heterogeneous. Both Games and Objects laid on the tables are located between these two extremes (i.e., Animals and Countryside), in the middle of this continuum. Finding the differences between them requires exploring the characteristics of their communities.

5.2.2. Internal and external relations of network communities

H2 refers to the interconnection between the members of a community and the links they maintain with other communities in the network. Section 4.2 has already explained that these relations are measured by calculating the EXTINT ratio, where a value close to 1 indicates that the connections between the members of a community are similar to those established towards the outside of the community. This measure, therefore, is determined by the inward or outward degree, and needs to be interpreted together with modularity, which refers to how network nodes are grouped into communities and how these communities are defined. A network is closely interconnected when it has a high EXTINT ratio and a high degree of modularity.

According to the initial hypotheses, as Table 7 shows, the community of Animals is interconnected more towards the outside than towards the inside (EXTINT ratio of 0.63) than those of Games and Countryside (both with an EXTINT ratio of 0.43). Although Objects laid on the table records the highest EXTINT ratio (0.95), this figure should be interpreted in relation to the lack of modularity (0.29). In other words, as opposed to Animals (modularity of 0.45) and Games and Countryside (both with a modularity of 0.52), Objects laid on the table is not arranged into defined communities. The nodes tend to be connected with all the other nodes in the network (both those in its community and those in others) because there is no clearly defined structure, which is consistent with this category’s ad hoc nature.

Table 7. Measures of association in each network’s communities

A close study of the external and internal degree reveals that Animals and Objects laid on the table have more edges towards the outside than towards the inside (1686 internal ones/2352 external ones, and 600 internal ones/1030 external ones, respectively), whereas the links towards the inside in Games and Countryside are more numerous (1122 internal ones/812 external ones, and 1430 internal ones /862 external ones, respectively). Nevertheless, the average for internal strength is higher in all cases.

5.2.4. Lexical access and negotiating the network

The final hypothesis at mesoscopic level (H4) refers to the part of the network that each informant traverses. In the more closely interconnected category, with internal and external relations between communities, the speakers may move around more within them, whereby each informant visits a higher percentage of their constituent nodes. By contrast, a participant in the more disperse and heterogenous categories may be confined either to a single community or to a reduced number of nodes.

With a view to analysing the participants’ behaviour, we may consider the measure of the percentage of the network (PNet) the participants visit in each cluster (Table 8). The participants in Animals and Objects laid on the table visit around twice the amount of the network compared to Games and Countryside. In other words, each participant visits a much more extensive part of the network during the task. In turn, the histogram (Figure 1) presents the PNet data: the y-axis reports the number of participants and the x-axis the percentage of the network each one of them visits. Animals records the highest percentage of the network visited (with a highly representative figure of 100 participants negotiating 8% of the network compared to the peaks for Countryside of 60 informants visiting barely 2.5%). These data depict a continuum from the natural category through to the schema: Animals is the category in which an informant visits a greater proportion of the network, followed closely by Objects laid on the table, Games and, finally, Countryside.

Table 8. Gaussian curve averages for each category in the histogram

Figure 1. PNet histogram.

5.3. Microscopic level

5.3.1. Relations between the central node and other nodes

Table 9 presents the measures considered for verifying H5, regarding the relations between the central node and all the other nodes in each network. The significance of the most central word is evaluated through the values corresponding to the three strongest nodes (-2) in each category, and an asterisk indicates those nodes considered to be central. This is not the only way of measuring a node’s significance within the overall network, as other relevant values are frequency (indirectly represented in strength), age of acquisition (Steyvers & Tenenbaum, Reference Steyvers and Tenenbaum2005) and lexical availability (Hernández Muñoz & Tomé, Reference Hernández Muñoz, Tomé and del Barrio2017). All these mathematically based values could represent aspects of the prototype, although the conceptualization of the last one of these is based on cognitive attributes that need not necessarily correspond to a network’s centrality, especially in more delayed approaches to abstraction (Geeraerts, Reference Geeraerts and Geeraerts2006).

Table 9. Measures of association for the three strongest nodes in each category

Objects laid on the table creates the network with the most salient node in terms of centrality and the strongest connections. Tablecloth is the object that best fulfils the contextual category’s purpose (centrality 0.41). Nevertheless, the hypothesis is not fully confirmed in the case of Animals: centrality is shared among several members of the central community, all of which are visited very frequently (the three indices considered are similar). Nonetheless, these two categories with a more taxonomic structure have central nodes with a higher number of overall strength (over 0.2) than Games and Countryside, where the concept of central node is diluted (below 0.2). It is still significant, however, that tree is the most central category in this last category, as it has high visual salience, and at the same appears as a hyponym in animals, which requires the abstraction of specific features and gives rise to one of the communities that are part of the schema’s framework.

5.3.2. Paths traversing the central node

H6 addresses the likelihood that a short path between two nodes traverses what is considered to be the central node. As noted, this characteristic is measured by the indicator betweenness centrality (BC) (Table 10).

Table 10. Betweenness centrality for the central nodes in each category

According to these data, the central node that is traversed by more short paths is the ad hoc category Objects laid on the table, followed by the schema Countryside, the radial Games and, finally, by Animals. Speakers in the ad hoc category return more often to the item that best fulfils the category’s purpose (a tablecloth is the first object laid on a table before a meal, and which therefore orchestrates its construction. Contrary to the initial hypotheses, the informants in the natural category tend not to traverse the central node, as accessing a very dense network, with clearly defined communities (see Section 5.2) and several nodes with a high degree of centrality (see Section 5.3.1), speakers have numerous possible paths available to them, which means they do not need to traverse the same node.